{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Window Splitters in Sktime\n",
    "\n",
    "In this notebook we describe the window splitters included in the [`sktime.forecasting.model_selection`](https://github.com/alan-turing-institute/sktime/blob/master/sktime/forecasting/model_selection/_split.py) module. These splitters can be combined with `ForecastingGridSearchCV` for model selection (see [forecasting notebook](https://github.com/alan-turing-institute/sktime/blob/master/examples/01_forecasting.ipynb)). \n",
    "\n",
    "**Remark:** It is important to emphasize that for cross-validation in time series we can not randomly shuffle the data as we would be leaking information.\n",
    "\n",
    "**References:**\n",
    "- [Cross-validation: evaluating estimator performance](https://scikit-learn.org/stable/modules/cross_validation.html#cross-validation)\n",
    "- [Cross-validation for time series](https://robjhyndman.com/hyndsight/tscv/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preliminaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from matplotlib.ticker import MaxNLocator\n",
    "\n",
    "from sktime.datasets import load_airline\n",
    "from sktime.forecasting.base import ForecastingHorizon\n",
    "from sktime.forecasting.model_selection import (\n",
    "    CutoffSplitter,\n",
    "    ExpandingWindowSplitter,\n",
    "    SingleWindowSplitter,\n",
    "    SlidingWindowSplitter,\n",
    "    temporal_train_test_split,\n",
    ")\n",
    "from sktime.utils.plotting import plot_series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data\n",
    "\n",
    "We use a fraction of the Box-Jenkins univariate airline data set, which shows the number of international airline passengers per month from 1949 - 1960. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Period\n",
       "1949-01    112.0\n",
       "1949-02    118.0\n",
       "1949-03    132.0\n",
       "1949-04    129.0\n",
       "1949-05    121.0\n",
       "Freq: M, Name: Number of airline passengers, dtype: float64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We are interested on a portion of the total data set.\n",
    "# (for visualisatiion purposes)\n",
    "y = load_airline().iloc[:30]\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plot_series(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Window Splitters\n",
    "\n",
    "Now we describe each of the splitters.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A single train-test split using `temporal_train_test_split`\n",
    "\n",
    "This one splits the data into a traininig and test sets. You can either (i) set the size of the training or test set or (ii) use a forecasting horizon."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# setting test set size\n",
    "y_train, y_test = temporal_train_test_split(y=y, test_size=0.25)\n",
    "fig, ax = plot_series(y_train, y_test, labels=[\"y_train\", \"y_test\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# using forecasting horizon\n",
    "fh = ForecastingHorizon([1, 2, 3, 4, 5])\n",
    "y_train, y_test = temporal_train_test_split(y, fh=fh)\n",
    "plot_series(y_train, y_test, labels=[\"y_train\", \"y_test\"]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Single split using `SingleWindowSplitter`\n",
    "\n",
    "This class splits the time series once into a training and test window. Note that this is very similar to `temporal_train_test_split`.\n",
    "\n",
    "Let us define the parameters of our fold:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set splitter parameters\n",
    "window_length = 10\n",
    "fh = ForecastingHorizon([1, 2, 3])\n",
    "fh_length = len(fh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Folds = 1\n"
     ]
    }
   ],
   "source": [
    "cv = SingleWindowSplitter(window_length=window_length, fh=fh)\n",
    "\n",
    "n_splits = cv.get_n_splits(y)\n",
    "print(f\"Number of Folds = {n_splits}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us plot the unique fold generated. First we define some helper functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_folds_arrays(y, cv):\n",
    "    \"\"\"Store folds as arrays.\"\"\"\n",
    "    n_splits = cv.get_n_splits(y)\n",
    "\n",
    "    windows = np.empty((n_splits, window_length), dtype=np.int)\n",
    "    fhs = np.empty((n_splits, fh_length), dtype=np.int)\n",
    "\n",
    "    for i, (w, f) in enumerate(cv.split(y)):\n",
    "        windows[i] = w\n",
    "        fhs[i] = f\n",
    "    return windows, fhs\n",
    "\n",
    "\n",
    "def get_y(length, split):\n",
    "    \"\"\"Creates a constant level vector based on the split.\"\"\"\n",
    "    return np.ones(length) * split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we generate the plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mloning/.conda/envs/sktime-dev/lib/python3.7/site-packages/ipykernel_launcher.py:21: UserWarning: FixedFormatter should only be used together with FixedLocator\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "windows, fhs = get_folds_arrays(y, cv)\n",
    "\n",
    "window_color, fh_color = sns.color_palette(\"colorblind\")[:2]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=plt.figaspect(0.25))\n",
    "for i in range(n_splits):\n",
    "    ax.plot(np.arange(len(y)), get_y(len(y), i), marker=\"o\", c=\"lightgray\")\n",
    "    ax.plot(\n",
    "        windows[i], get_y(window_length, i), marker=\"o\", c=window_color, label=\"Window\"\n",
    "    )\n",
    "    ax.plot(\n",
    "        fhs[i], get_y(fh_length, i), marker=\"o\", c=fh_color, label=\"Forecasting horizon\"\n",
    "    )\n",
    "ax.invert_yaxis()\n",
    "ax.yaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "ax.set(\n",
    "    title=\"SingleWindowSplitter Fold\",\n",
    "    ylabel=\"Window number\",\n",
    "    xlabel=\"Time\",\n",
    "    xticklabels=y.index,\n",
    "    ylim=(-1, 1),\n",
    ")\n",
    "# remove duplicate labels/handles\n",
    "handles, labels = [(leg[:2]) for leg in ax.get_legend_handles_labels()]\n",
    "ax.legend(handles, labels);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[27, 28, 29]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fhs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[17, 18, 19, 20, 21, 22, 23, 24, 25, 26]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "windows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sliding windows using `SlidingWindowSplitter`\n",
    "\n",
    "This splitter generates folds which move with time. The length of the training and test sets for each fold remains constant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Folds = 18\n"
     ]
    }
   ],
   "source": [
    "cv = SlidingWindowSplitter(window_length=window_length, fh=fh, start_with_window=True)\n",
    "\n",
    "n_splits = cv.get_n_splits(y)\n",
    "print(f\"Number of Folds = {n_splits}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mloning/.conda/envs/sktime-dev/lib/python3.7/site-packages/ipykernel_launcher.py:18: UserWarning: FixedFormatter should only be used together with FixedLocator\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "windows, fhs = get_folds_arrays(y, cv)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=plt.figaspect(0.25))\n",
    "for i in range(n_splits):\n",
    "    ax.plot(np.arange(len(y)), get_y(len(y), i), marker=\"o\", c=\"lightgray\")\n",
    "    ax.plot(\n",
    "        windows[i], get_y(window_length, i), marker=\"o\", c=window_color, label=\"Window\"\n",
    "    )\n",
    "    ax.plot(\n",
    "        fhs[i], get_y(fh_length, i), marker=\"o\", c=fh_color, label=\"Forecasting horizon\"\n",
    "    )\n",
    "ax.invert_yaxis()\n",
    "ax.yaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "ax.set(\n",
    "    title=\"SlidingWindowSplitter Folds\",\n",
    "    ylabel=\"Window number\",\n",
    "    xlabel=\"Time\",\n",
    "    xticklabels=y.index,\n",
    ")\n",
    "# remove duplicate labels/handles\n",
    "handles, labels = [(leg[:2]) for leg in ax.get_legend_handles_labels()]\n",
    "ax.legend(handles, labels);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Expanding windows using `ExpandingWindowSplitter`\n",
    "\n",
    "This splitter generates folds which move with time. The length of the training set each fold grows while test sets for each fold remains constant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Folds = 18\n"
     ]
    }
   ],
   "source": [
    "cv = ExpandingWindowSplitter(window_length=window_length, fh=fh, start_with_window=True)\n",
    "\n",
    "n_splits = cv.get_n_splits(y)\n",
    "print(f\"Number of Folds = {n_splits}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_expanding_window_arrays(y, cv):\n",
    "    \"\"\"Store folds as arrays.\"\"\"\n",
    "    n_splits = cv.get_n_splits(y)\n",
    "\n",
    "    windows = []\n",
    "    fhs = np.empty((n_splits, fh_length), dtype=np.int)\n",
    "\n",
    "    for i, (w, f) in enumerate(cv.split(y)):\n",
    "        windows.append(w)\n",
    "        fhs[i] = f\n",
    "    return windows, fhs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mloning/.conda/envs/sktime-dev/lib/python3.7/site-packages/ipykernel_launcher.py:22: UserWarning: FixedFormatter should only be used together with FixedLocator\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "windows, fhs = get_expanding_window_arrays(y, cv)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=plt.figaspect(0.25))\n",
    "for i in range(n_splits):\n",
    "    ax.plot(np.arange(len(y)), get_y(len(y), i), marker=\"o\", c=\"lightgray\")\n",
    "    ax.plot(\n",
    "        windows[i],\n",
    "        get_y(window_length + i, i),\n",
    "        marker=\"o\",\n",
    "        c=window_color,\n",
    "        label=\"Window\",\n",
    "    )\n",
    "    ax.plot(\n",
    "        fhs[i], get_y(fh_length, i), marker=\"o\", c=fh_color, label=\"Forecasting horizon\"\n",
    "    )\n",
    "ax.invert_yaxis()\n",
    "ax.yaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "ax.set(\n",
    "    title=\"ExpandingWindowSplitter Folds\",\n",
    "    ylabel=\"Window number\",\n",
    "    xlabel=\"Time\",\n",
    "    xticklabels=y.index,\n",
    ")\n",
    "# remove duplicate labels/handles\n",
    "handles, labels = [(leg[:2]) for leg in ax.get_legend_handles_labels()]\n",
    "ax.legend(handles, labels);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multiple splits at specific cutoff values - `CutoffSplitter`\n",
    "\n",
    "With this splitter we can manually select the cutoff points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Folds = 4\n"
     ]
    }
   ],
   "source": [
    "# Specify cutoff points (by array index).\n",
    "cutoffs = np.array([10, 11, 17, 23])\n",
    "\n",
    "cv = CutoffSplitter(cutoffs=cutoffs, window_length=window_length, fh=fh)\n",
    "\n",
    "n_splits = cv.get_n_splits(y)\n",
    "print(f\"Number of Folds = {n_splits}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "windows, fhs = get_folds_arrays(y, cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mloning/.conda/envs/sktime-dev/lib/python3.7/site-packages/ipykernel_launcher.py:18: UserWarning: FixedFormatter should only be used together with FixedLocator\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "windows, fhs = get_folds_arrays(y, cv)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=plt.figaspect(0.25))\n",
    "for i in range(n_splits):\n",
    "    ax.plot(np.arange(len(y)), get_y(len(y), i), marker=\"o\", c=\"lightgray\")\n",
    "    ax.plot(\n",
    "        windows[i], get_y(window_length, i), marker=\"o\", c=window_color, label=\"Window\"\n",
    "    )\n",
    "    ax.plot(\n",
    "        fhs[i], get_y(fh_length, i), marker=\"o\", c=fh_color, label=\"Forecasting horizon\"\n",
    "    )\n",
    "ax.invert_yaxis()\n",
    "ax.yaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "ax.set(\n",
    "    title=\"CutoffSplitter Folds\",\n",
    "    ylabel=\"Window number\",\n",
    "    xlabel=\"Time\",\n",
    "    xticklabels=y.index,\n",
    ")\n",
    "# remove duplicate labels/handles\n",
    "handles, labels = [(leg[:2]) for leg in ax.get_legend_handles_labels()]\n",
    "ax.legend(handles, labels);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10],\n",
       "       [ 2,  3,  4,  5,  6,  7,  8,  9, 10, 11],\n",
       "       [ 8,  9, 10, 11, 12, 13, 14, 15, 16, 17],\n",
       "       [14, 15, 16, 17, 18, 19, 20, 21, 22, 23]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "windows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[11, 12, 13],\n",
       "       [12, 13, 14],\n",
       "       [18, 19, 20],\n",
       "       [24, 25, 26]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fhs"
   ]
  }
 ],
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